import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from sklearn.linear_model import LogisticRegression

df_train = pd.read_csv('../Datasets/Breast-Cancer/breast-cancer-train.csv')
print(df_train)

df_test = pd.read_csv('../Datasets/Breast-Cancer/breast-cancer-test.csv')
print(df_test)

df_test_negative = df_test.loc[df_test['Type'] == 0][['Clump Thickness', 'Cell Size']]
df_test_positive = df_test.loc[df_test['Type'] == 1][['Clump Thickness', 'Cell Size']]
print(df_test_negative)
print(df_test_positive)

plt.scatter(df_test_negative['Clump Thickness'], df_test_negative['Cell Size'], marker = 'o', s = 200, c = 'red')
plt.scatter(df_test_positive['Clump Thickness'], df_test_positive['Cell Size'], marker = 'x', s = 150, c = 'black')
plt.xlabel('Clump Thickness')
plt.ylabel('Cell Size')
#plt.show()

intercept = np.random.random([1])
coef = np.random.random([2])
print(intercept)
print(coef)
lx = np.arange(0, 12)
ly = (-intercept - lx * coef[0]) / coef[1]
plt.plot(lx, ly, c = 'yellow')
plt.scatter(df_test_negative['Clump Thickness'], df_test_negative['Cell Size'], marker = 'o', s = 200, c = 'red')
plt.scatter(df_test_positive['Clump Thickness'], df_test_positive['Cell Size'], marker = 'x', s = 150, c = 'black')
plt.xlabel('Clump Thickness')
plt.ylabel('Cell Size')
plt.show()

lr = LogisticRegression()
print(lr)
lr.fit(df_train[['Clump Thickness', 'Cell Size']][:10], df_train['Type'][:10])
print('Testing accuracy (10 training samples):', lr.score(df_test[['Clump Thickness', 'Cell Size']], df_test['Type']))
intercept = lr.intercept_
coef = lr.coef_[0, :]
print(intercept)
print(coef)
ly = (-intercept - lx * coef[0]) / coef[1]
plt.plot(lx, ly, c = 'green')
plt.scatter(df_test_negative['Clump Thickness'], df_test_negative['Cell Size'], marker = 'o', s = 200, c = 'red')
plt.scatter(df_test_positive['Clump Thickness'], df_test_positive['Cell Size'], marker = 'x', s = 150, c = 'black')
plt.xlabel('Clump Thickness')
plt.ylabel('Cell Size')
plt.show()

lr = LogisticRegression()
lr.fit(df_train[['Clump Thickness', 'Cell Size']], df_train['Type'])
print('Testing accuracy (all training samples):', lr.score(df_test[['Clump Thickness', 'Cell Size']], df_test['Type']))
intercept = lr.intercept_
coef = lr.coef_[0, :]
ly = (-intercept - lx * coef[0]) / coef[1]
plt.plot(lx, ly, c = 'blue')
plt.scatter(df_test_negative['Clump Thickness'], df_test_negative['Cell Size'], marker = 'o', s = 200, c = 'red')
plt.scatter(df_test_positive['Clump Thickness'], df_test_positive['Cell Size'], marker = 'x', s = 150, c = 'black')
plt.xlabel('Clump Thickness')
plt.ylabel('Cell Size')
plt.show()
